In RIS, the phase shift of each RIS element can be adjusted to improve the performance of RIS-assisted wireless communication systems.
Different from conventional communications, it is of importance to jointly optimize the passive beamforming (phase shift matrices at the RIS) and active beamforming (beamforming at the multi-antenna transmitter)
[1]}, [2]}.
To solve the complicated joint passive and active beamforming, deep learning (DL) has been used to design the best reflection matrix
of RIS elements in indoor communication environments [3]}.
In practice, similar to multi-hop relaying systems, multiple
RISs can be used to overcome severe signal blockage between the BS and users to achieve
better service coverage.
The authors in [4]} presented a multi-hop RIS-assisted communication scheme to overcome the
severe propagation attenuations and improve the coverage range at Terahertz (THz) band frequencies, where
the hybrid design of transmit beamforming at the BS and phase shift matrices is obtained by
the advances of RL.
Due to the high complexity of using centralized RL, FRL can be utilized to solve the joint passive and active beamforming problem, where all users can individually optimize the phase shift matrices and transmit beamforming via RL, and the BS broadcasts the aggregated learning model to all users.
